In this special episode Glenn is joined by Nate Saperia to answer the 10 most burning questions in FP&A. Nate brings nearly 20 years of finance experience including at Accordion, Spruce Finance, Hess Corporation and GE. At Saperia Consulting Nate specializes in real-time dashboards, financial planning, and interim CFO/FP&A leadership.
The questions:
Q1: How can I use AI in FP&A?
Q2: What do you think of FP&A solutions?
Q3: How can you get to driver based decision making
Q4: Fastest levers FP&A teams can pull with margin pressures rising?
Q5: How can an FP&A function trust the financial data it’s using when it doesn’t control the data?
Q6: Should the CTO or CFO own the data?
Q7. Skills to get from M&A Financial due diligence to FP&A?
Q8. FP&A Internship, what advice?
Q9. Things I wish I had known earlier in FP&A?
Q10. What’s the future of Excel in FP&A?
Glenn Hopper:
Today. Welcome, everyone. Thank you for joining us for the special event at FP&ACon. It’s brought to you by Data Rails. I’m Glenn Hopper, and today’s session is not only part of the conference lineup, it’s also being recorded as a special episode of the FP&A Today podcast. The title of today’s session is Top 10 Burning fp NA Questions. And over the next hour, we’re gonna tackle the issues that finance leaders are wrestling with right now from AI driven forecasting to rolling forecasts, margin pressure, and the future of Excel. So the idea here is kind of a fast-paced format, some strong opinions, commitment to keep things highly practical. And, um, unfortunately, Swati is not gonna be able to join us as she’s had something, uh, come up at the last minute. So, um, Swati, our thoughts are with you and, and we’ll do our best in your absence in SWAT’s absence.
It’s my pleasure to introduce our guest panelists today. Nate Superior is the founder of Superior Consulting, where he helps private equity backed firms scale fp and a and operational finance capabilities. A former managing director at Accordion, a leading consulting firm dedicated to the office of the CFO. He has supported dozens of companies in building more effective forecasting, reporting and planning processes. Earlier in his career, Nate held fp and a in corporate finance roles at Chopped Spruce Power and ge. He’s a strong advocate for integrating Power BI and automation into finance workflows to drive efficiency and enhance insights and enable teams to focus on higher value analysis. So, Nate, I read the bio there, but maybe in your own words, give us a little bit about your fp and a philosophy and your current focus.
Nate Saperia:
Yeah, so there, there are a lot of logos on there and thanks for the, the background. Glenn, I’m gonna maybe a little bit more storytelling because I’m gonna, I’m gonna talk about how important storytelling is today, so I better, you know, back up my word with that. I’ve been in finance for about 20 years and doing FP&A a little over half of that time. I started out in consulting in a very academic environment, went to to business school to focus more on corporate finance, and then sort of fell into FP&A. After that, I had joined a renewable energy startup in a capital markets position, and the head of FP&A left and they said, Nate, you’re good with finance, you’re good with Excel. Why don’t you just do the FP&A as well on top of capital markets? And I said, okay.
I, I don’t know exactly what that means, but I’ll give it a shot. I had some good learning experiences during that time. I remember, you know, a new investor coming in and they asked for new recording and said, give us your KPIs monthly. And I, she basically went to our CFO and I said, I should know this, but what’s a KPI, even though I’m head of FP&A. And he said, I don’t know. Let’s go ask the CEO. We’re gonna have to get over this embarrassment together, but let’s go ask. And that’s, that’s sort of what it was like back then for a lot of us. So I’m really excited that there are a lot more resources like this conference, uh, that datarails is sponsoring and just a lot more resources in general. So I learned fp and a on my own. I’ve done a budget every year for the past 10 years.
I think last fall was the first time I didn’t do it and moved through a couple different head of fp and a positions shopped and First Para you said. And then again, fell into a role actually at a consulting firm doing what had been termed at the time Strategic Fines, which is really just strategic financial planning and analysis with a little bit of m and a layered on top over time. You know, I’ve learned, you asked about my philosophy on FP&A, my philosophy is at, it’s, at the end of the day, it’s a storytelling business. It’s a business partnering business. A lot of junior folks focus on the technical skills, the forecasting, the modeling, and that’s a huge part of it. But those are just tools. They’re tools to get you to the place where you can make informed business decisions and give your business partners and your leadership the information to drive the company in the right direction. So I did that at Accordion for seven years advising private equity backed companies, and very recently started my own firm so I could specialize industry-wise on consulting and accounting. So that’s where I am right now, figuring out, out, out day to day and running my own business.
Glenn Hopper:
I love talking to other, uh, og fp and a folks because I’ve started my career, actually I started in marketing and the reason I got into finance fresh out of MBA program, the reason I got into finance is I was a product manager of this really cool product, but I just, we didn’t have enough money in budget to do all the stuff I wanted. So I asked the head of marketing, Hey, let me look at our budget. You keep saying we don’t have it, what do we have? And so I got in and, you know, we’d find money and, and, uh, help with the budget. And soon I, I kind of transitioned into running the marketing budget, got poached by the, uh, COO to do kind of a, didn’t roll into the CFO, my first finance position. After that, I, I rolled into the COO as his sort of personal budget manager.
And, um, back then though, fp and a wasn’t a a profession, we just were the finance procurement guy. Like, it was a weird mix of things, <laugh>. And, um, I would joke people, ’cause it was a pretty big promotion when I, I moved over and people would say, what, how did you get that promotion? And I said, I don’t know. I’m, I’m pretty good at Excel and PowerPoint. And I thought that was, I thought I was joking around that. But if you think about where fp and a today is, what that means is I could model this stuff and then I could present it for the storytelling. And we didn’t even talk about storytelling back then, but it’s become such a big important part of what we do.
Nate Saperia:
Yeah, sounds like a very, very similar background. And I think that’s actually even today, even though that there, there’s a lot more resources out there, I I think you’re still gonna find a similar dynamic in people who are drawn fp and a. It’s the people who can’t stop themselves from asking the questions and just want to dig deeper. And, you know, that led me to skills like learning VBA, learning how to do very complex Excel models. But the reason behind it is I just needed an answer. I couldn’t stop myself from getting an answer. And I still think that’s true of really good fp a folks these days.
Glenn Hopper:
Alright, so that actually is gonna transition right into my first question here, because I think about the way that probably most people on, on the webinar here came up. It was ’cause we’re really good at excel and we could do really cool things and we could build models and, you know, we figured out this sort of complex thing and like you said, go learn VBA and, and maybe access back in the day or you know, all the other tools that we, uh, <laugh> that we learned. But now generative AI is around, and ev listeners of the podcast know, I’m gonna spend a lot of time talking about ai, but not really. We’re gonna <laugh> we’re gonna mix it up, but of course I’m gonna lead with an AI question. And a big part of what I do these days is show fp and a folks how they can use generative AI to do things that are core to our job.
Uh, you know, variance analysis, looking for anomalies, uh, doing forecasts, doing more complicated forecasts than maybe a typical, you know, regression forecast that we might, might do in Excel. And it’s pretty amazing what you can do with generative ai and we’re hearing every day about AI agents and you know, this is gonna be the future. I wonder with your skillset, and I don’t know how closely you’ve sort of followed what we can do with generative AI versus in, in traditional tools, but I mean, if you’re reading the, the tea leaves right now, what are your thoughts? Are AI agents eventually gonna take over like forecasting or do you think human intuition will always have the final say around that?
Nate Saperia:
So I’d say I know enough AI to be dangerous. I think I know a fair amount, which means I’m just barely scratching surface. And I wa I was thinking about this today, and this is gonna go a bit off off topic for fp and a for a little bit, but I was reading a Wall Street Journal article by Joanna Stern who’s sort of the tech reporter for the Wall Street Journal. And if you don’t follow her, she’s amazing. But she’d made this three minute, uh, video clip of her and a robot assistant completely generated by ai and it was amazing. It was like nothing you’ve ever seen. And it was impressive how far the, the technology has come, but had a great line at the end said, so think you can paste a script and now pops a Netflix hit cute, very cute that you think you can do that.
It actually took them, she’d hired a video producer who also was an expert in AI and prompting, and they’d done a thousand clips to get down to a three minute video. So there’s always gonna be that, that human element. And I feel the same way in finance. You know, I do use AI a lot on a day-to-day basis, and man, it comes back with some crazy stuff sometimes. So you have to take it with a grain of salt and make sure it’s not hallucinating. I think it’s gonna have huge impacts. It’s going to take out a lot of the heavy lift. How big and how soon those impacts are may vary industry by industry. Like for example, in a SaaS company where you’re doing high volume recurring revenue, it’s probably gonna be pretty good. Uh, right off the bat, if you feed it a ton of data and it understands the seasonal trends, uh, it’s probably gonna be pretty good.
You’re still gonna have to monitor it. I come from advising professional services firms where revenue is really counted and we’re dealing with people not software. And you have things like key, key person risks and it’s all relationship driven. So there’s a spectrum of how quickly it’s going to be able to disrupt. I think where I see the, the immediate use cases is the really manual tasks like data cleansing. And I, I know we’re gonna talk about BI and data integration later on, but you know, data cleansing is the basis for everything. And AI can just do so much more than a human can, whether it’s, you know, deduplicating a set of customer records to get master customer mapping, you know, taking email, phone number name, credit card number, and going through and quickly cleaning something would take weeks for humans to do. It can do an hour. So I, I think that’s one really use case. It’s not that sexy, but man will have that have an impact.
Glenn Hopper:
Yeah, I mean, what, so, you know, 80% of, uh, of data science and analytics is cleaning the data. And if you can <laugh>, if you can automate that, that and then you’re actually adding value and not just doing the, the data cleaning part. And that’s, I a hundred percent agree there. You know, I guess, uh, just to go a a level deeper on that, as a consultant in my day job, I too am in, in an advisory services role. And I think that that industry is having a, we’re having a bit of an existential crisis because if you look at like the McKinsey’s of the world and think about the value they had, well if these LLMs have read, you know, thousands and thousands of McKinsey reports and or, and equivalents and, you know, the ability to swap out a, a consultant for an AI as, uh, you know, it, it’s, it is an existential threat. Maybe not tomorrow, like you said. I mean there’s still, we have the hallucination issue and all that in the short term though, the efficiency gains that come from it, I mean, I feel like I’m wearing an exoskeleton half the time just because stuff that used to take, you know, hours I can now do in 15 minutes.
Nate Saperia:
Yeah, I mean, I, I’m trying to imagine starting my own per one person firm with that without AI and k posa, like my, my best friend, which is scary, kind of said I, let’s just say it’s my associate, like, it, it gets me 80 to 90% of the way there and an associate would. But that, that’s actually my biggest concern with it. I, in consulting in, in AL services, we used to talk about pyramids and how pyramid team structures and how you have a leader on top managers in the middle and then doers at the bottom. We’re starting to talk more about diamonds and those doers, the work that they’re doing. Yes, it’s very manual. Yes, it can be very, very boring at times, but it also gives, you know, first year analysts, second year analysts an opportunity to come into a business and it, it might be very manual, very boring work, but they’re also learning the business at the same time. If you take that layer out, where does your next level of managers come from and where does your next level of leadership come from? And, and that’s my, my biggest concern is, yeah, I can do it faster, but what, what does that mean for leadership? Five years?
Glenn Hopper:
Yeah, yeah, yeah. And of course we could go on all day and, uh, if I followed my, uh, <laugh>, what I wanted to, uh, talk about mostly these days, it seems like I would stay on that path, but I do, I promise fast paced. So we would <laugh> get going, fp and a today is brought to you by Data Rails. The world’s number one fp and a solution Data rails is the artificial intelligence powered financial planning and analysis platform built for Excel users. That’s right, you can stay in Excel, but instead of facing hell for every budget month end close or forecast, you can enjoy a paradise of data consolidation, advanced visualization reporting and AI capabilities, plus game changing insights, giving you instant answers and your story created in seconds. Find out why more than a thousand finance teams use data rails to uncover their company’s real story. Don’t replace Excel, embrace Excel, learn more@datarails.com. I do wanna shift, let’s go from gen AI to more bi sort of, even classical machine learning could be in there, but I’m thinking of self-service BI tools and I’m wondering from your standpoint, what’s the playbook for embedding those self-service BI tools into like, like power BI into finance without creating chaos or duplication. I mean, it’s a lot when you’re, you know, if you’re Excel based and you’re moving into, uh, bringing in these tools.
Nate Saperia:
Yeah, so I, I come across these questions a lot and my clients don’t like the answer. Uh, they come in saying, let’s go straight to ai. And I said, well, wait a second. You’re, you don’t even have a data warehouse. You’re hosting everything in Excel. Your data’s all over the place. I mean, I’m, I’m going back to the AI and the cleansing stuff, but it, it’s really crawl, walk, run. You need to get that data clean. Number one, you need to define what matters for your business in terms of metrics and KPIs and get everyone on the same page about it. I remember I had a, a large consumer retail client, like multi-billion dollar revenue, and I was building out their entire sales BI platform, but we, you know, we couldn’t start building until everyone was on the same page. And I remember getting the entire C-suite in a room, and we’re talking about consumer retail is sales is orders, number of orders times average order value.
And they, they could not agree on what an order is, like literally how do you define what an order is? And it took us two and a half hours of beating a dead horse there for everyone to come to the same agreement on how we were defining an order. And it would’ve been complete chaos if we’d just implemented, you know, just written, written the code, written the decks in Power BI without having gone through this exercise. So that, that’s a crawl, walk, run. And it’s not a sexy pitch. And it’s, but it’s completely necessary once you have that in place. The technology has advanced a lot. I recommend, you know, 10 years ago we try and build a data cube in Power bi. We’ve moved to a place where it’s easy enough to set up an enterprise data warehouse that you should really be doing that. And then, you know, the BI should be a layer on top, in which case I prefer Power bi, but they’re, they’re all gonna work as long as, you know, the heavy lift is happening outside of the BI tool. And even corporate performance management tools, they’re going to work the same, you know, data rails is an example, but there are lots of them out there. They need to be a layer on top of a single source of truth. And that’s really the first step.
Glenn Hopper:
Yeah. And really, I mean, cannot stress enough and it’s, no, nobody loves this <laugh> when you come in, especially as a consultant and they’re expecting you to wave an AI wand and, and fix their data problems, but data is the foundation and um, and it has to start there. So I guess kind of following up on that, from your perspective, what are the absolute must haves in terms of data quality for fp and a teams in 2025?
Nate Saperia:
You have to have what I call a metrics matrix. And what you need to do is go through every possible metric that your business could have, then define which ones matter the most. And those are your KPIs. And KPIs need to be not just reportable, but actionable. They mean that means they’re levers. You don’t just know what happened and how you’re performing versus say your budget. But you need, it needs to be something where it can say, okay, traffic was down in a store because of weather or because it was just a slow week. How can we real time, is there a marketing tool we can use to, you know, Thursday, Friday drive up same store sales or traffic so we can end this week on, on a strong note and not have, you know, get a report on Monday saying we had a bad week, but actually midweek we can change the outcome of that week.
Glenn Hopper:
Yeah. And it’s, to me, it just goes back to sort of the levels of analytics that you’re doing. So the first thing to get people to that kind of the base, it’s like you were said when you, uh, <laugh> went with the CFO and you had to find what are KPIs. So you define what the KPIs are, and then you get this the descriptive layer of, okay, this is the dataset that we have and this is what we see in it. This is what we can explain. It’s, it’s the understanding the why. Like once you get those KPIs identified and you can drill down and drill down. And that’s kinda the job of fp and a is to get to the why, but why, but why get down to that root cause. But once you really nail that and then you get to the predictive and you say, okay, well now we see kind of the trends and we understand why it’s happening.
So now we can forecast based on the data, we can sort of predict where, where things are going. But to your point on the levers, when you really understand and you see those correlations between rainy days and, and sales or what, whatever the correlation is that then you can actually have prescriptive analytics where you’re using that, that those fp and a chops to say, okay, we see this, do this to change it. And then that’s where you start to be really, really valuable. And you go beyond that sort of historical, when you and I first started, uh, the historical like, you know, just backward looking reporting. Yeah.
Nate Saperia:
And it, it’s just an extension of, you know, back in the day it was all about Excel, but it’s really about what, what is my model telling me? And business partnering. And then one, one other thing I’ve noticed over my career, which was a really important insight is, you know, I’ve had situations where I’ve built these really great dashboards and I think it’s perfect and I put it out there, expanded the business partners to have like a, uh, moment, aha moment. And you watch it for a couple weeks and you are the number one user of the dashboard. And maybe there’s one other, our user that’s used it three times. Mm-hmm <affirmative>. Adoption is what matters. And sometimes you really have to meet your partners where they are. And that may be ’cause not just dropping a dashboard out there, but if you can have an email that goes out every morning that captures a snapshot of the dashboard, your, your business partners are much more likely to be living in their email, in their inboxes, putting out fires. If they get used to the cadence of most people click open every email they get from their business partners. So if they get ’em the cadence of seeing it every day, maybe then one day they start clicking through and actually getting to the dashboard. So there’s gonna take some adoption time and uh, you have to go outta your way and handhold. And that’s not your business partner’s fault. That’s your job as a partner.
Glenn Hopper:
Yeah. All good modelers I think maybe can lean towards this and I, uh, work like you worked for PE backed companies and you know how private equity is, and they just push you on the models and they want, you know, such levels of detail that you get. You can get lost in a model, especially if you’re not sort of embedded across other teams and you’re just, you’re at your desk, you’re at your computer just building these models. And, uh, I had a professor years ago tell me that, um, I had a, the habit of, uh, mistaking the map for the terrain, um, you know, just ’cause you get so tuned in at what, how can I make this model better? How can I, and it’s not, you know, you forget, well, that’s not reality, that’s just, you know, driving the model. But it’s a lot, it’s a lot of fun and it is some kind of control thing.
But I, the reason I I set that background is, you know, as levers of data and as people who are using data, we want as much of it as as we can get. Um, and then, uh, you know, another, uh, you know, the map for the terrain is is, is one maybe cliche, and another one is everyone has a plan until they get punched in the mouth. And I’m thinking about when you make your financial forecast and it looks, you’ve got the best model in the world, it looks like the best plan, but then you have to reforecast and all that. And I’m wondering for you, how do you decide between kind of those rolling forecasts that go with okay, sort of the Bayesian approach of we have new information now let’s adjust the model accordingly, or just using driver based scenario, uh, models and you know, each, I know each each has its merits, but I’d love your thought on both and kind of how often should each be updated or when to use which.
Nate Saperia:
Yeah. Uh, so I’ll get to that in a minute, but you touched on private equity for, for a minute. I, I want to talk about that as its its own base. Private equity folks, they are modelers, that’s what they were raised doing. So the bar is pretty high and they’re not gonna get into the market and folks, they’re not gonna get in the weeds with them probably, but they’re gonna pick apart your model. So it’s a, it’s a special scenario. Probably the same with VC in terms of rolling forecasts versus driver based. I don’t really see it as an either or. I fundamentally think all forecasts, all plans need to be driver based. And that’s going back to the whole point, is to be able to influence decisions that influence outcomes. And if you define those KPIs properly, then you know the levers that can influence outcomes.
So your forecast isn’t there to have something down on paper, it’s to let you know what things might look like in different scenarios. And the only way to do that is to tie it back to how the business actually operates. So a hundred percent everything needs to be driver based, but you should also be having, we’re at the point in time where through data queues, you can pretty quickly not just get your financials automated, but have your KPIs automated and each month the new KPIs are loaded in and you take a look at the, where the variances are and how does that influence your opinion of where things are going. So I, I think it’s both. I think it needs to be KKPI driver based, but you also need a full year’s visibility out. So the rolling forecast, you can have a just have a driver base rolling forecast. That’s my preferred method. But I’m curious whether you see any reasons that they’re mutually exclusive.
Glenn Hopper:
No, and honestly, I mean, to me, the annual budget is, that’s where you’re sitting down with all the drivers. You, you, you go and you get the sales plan and you know, okay, we’re hiring this many more salespeople, we’re opening in this new market and we’re going, and, you know, these are all the drivers that, okay, we know based on all things being equal in, in the prior year, you know, we’ve got our, our sort of, uh, trailing 12 months that we’re looking at and okay, this is our run rate and this is what we’re gonna base on. We know we’re making these changes. You maybe try to throw in some macroeconomic predictors, which that gets so dicey and, but you know, you’re, you’re trying to figure, okay, well we’re in a raising interest rates environment, so fewer people are gonna be taking out loans lower ca you know, you, you try to factor all that in, but then, you know, you get punched in the face and the economy doesn’t go the way you thought. And a lot of those drivers are scrapped. So I guess to me, you can, and it goes back to that map versus the terrain thing. You can go back to, well, let me now go change the drivers, or, mm, I don’t understand exactly what, you know, correlation versus causation. I don’t know what exactly is driving our, our us to miss our numbers, but I know the trend line and it’s pretty clear. So how do I decide between picking the trend line versus changing the drivers?
Nate Saperia:
Yeah. And I think that’s to totally valid and the best way to do, it’s probably look at like, look at both, and that’s where the, the human element comes in. I think one thing about budgets, I think it’s a very loaded term. I throw it around all the time just because that’s the term we use, but when I use the word budget, I’m talking about the annual operating plan. But a budget really is just an expense approval mechanism. But we, we shorthand budget to mean annual operating plan, which is really the strategy for the next year. And so I, I need to step back and tell, remind myself of the difference, even though we use budget as shorthands,
Glenn Hopper:
You know, who doesn’t know the difference? Bankers <laugh>, anytime you’re dealing with covenants and debt and everything, they want to know every, you know, the breakdown. It’s <laugh>. It’s like you’re seeing the same macro economic stuff I am. And we’re, you know, this is our new forecast. And of course you don’t hear about it when you’re exceeding budget. So what you learn is we’re gonna sandbag to the banks, is what we’re gonna do. <laugh>. Yeah,
Nate Saperia:
Yeah. Having, having a different lender model is, you know, that’s what it is. Yep.
Glenn Hopper:
I can’t believe I’m bringing this up again, but we’ve, we’ve been talking about digital transformation for three decades and like how if we transformed, how, how are we still talking about it? So I think transformation’s probably a bad label. It’s more of just digital evolution or just keeping up with the technology. But I know you’ve done fp and a transformations, and I think that an fp and a transformation kind of goes along hand in hand with what we’re looking at now of digital transformations of people trying to get ready for ai. And, and really we, we can even take generative AI out of the picture if I’m trying to move to a data-driven organization and you’re saying invest all this time and effort into, you know, whatever systems we need to put in place, whatever work needs to be done, the data dictionary and, and getting everybody on board with defining the specific KPIs with single source of truth and all that great stuff.
That’s all this work. And then, you know, so you’ve digitally transformed, I’ve come across this with, with management before who they see that they get their data Dr they get their data points, but maybe they’re still, well, I’ve been doing this for a million years and I trust my hunch, or, or whatever. So it’s, it can be sometimes hard to explain that ROI and maybe even more so now in the day of generative AI where people are getting more and more pressure. So I’m wondering, and you could talk about this from an fp and a transformation standpoint or just a, a digital transformation, but is there in your mind a good way to calculate and communicate ROI on this sort of transformation? Like I know businesses that they could be doing, you know, in the s and b space, maybe they’re doing 20, 25 million a year in revenue, they have zero fp and a, they just have the, the managing partners or whatever, or, you know, CEO are just going by what they think. Maybe they don’t even budget all the time. So if you’re trying to convince them you need to make the switch, it can be a hard sell and they wanna know, you know, what do I get out of it or what, how do I, I prove that it was worth the effort.
Nate Saperia:
This reminds me of a conversation I had when I was at my formal consultant firm and we hired someone from a much bigger consultancy and we’re getting to know each other. We had dinner together, and he was really focused on talking to me about big T transformation and little T transformation. And I hated that. I was like, that’s the most consultant speak thing I’ve ever heard. <laugh> <inaudible> transformation versus little T. But, uh, you know, I get what he was talking about now, like little T transformation is you’re just changing things and that’s it. It might be you’re improving the technology, but you’re not showing in ROI, there Big T transformation is showing the ROI, and the way I’ve generally tried to frame it is finance is generally viewed as a cost center and one of the lowest cost centers on the list of cost centers.
But there are ways to think about it as a profit center. I remember, you know, I, I did an internship that very large car manufacturer, and this is a little different than fp and a, it was treasury, but they were working on tax deals where there could be massive cost savings. And they like to talk about doing that as a profit center, if you can have massive cost savings that is generating profits. So in that instance, that finance team had paid for itself. So doing your best to quantify it. That’s the, the big key transformation is either cost savings. In revenue growth. Example I’ve done a lot is sales dashboards, whether it’s for the consumer retail, uh, firm, so they can make changes midweek or at my former consulting firm, getting the business development folks who are covering different accounts, getting them more visibility so that they could sell more. That’s how you have to sell it. You have to do your best to quantify now how much here is in cost savings. And that can be actual hard costs or that can be number of hours spent or show that there’s a real revenue generation opportunity there. And probably don’t tell them, probably don’t tell leadership. You want them to think of you as a profit center because they’re gonna think that’s ridiculous. But in your mind, that’s how you should think about it.
Glenn Hopper:
Well said. A couple of quick wins before we move on to the next section. Fastest levers, fp and a teams can pull when margin pressure is rising across the business. And I’m thinking, well, every year that could be an issue, but I’m thinking with tariffs and trade considerations, supply chain considerations, that’s probably top of mind for a lot of people right now. So margin in particular, any, any levers that come to mind to you that an fp and a team should identify and, and target in this kind of environment?
Nate Saperia:
Yeah, so can I completely reframe this question? Yeah. Okay. Because I, yeah, I was gonna, I was gonna do it either way.
Glenn Hopper:
So yeah, <laugh>, yeah, just go, just go like a politician, just have your script and answer what you wanted. Fine. I’m just,
Nate Saperia:
So, uh, before I start thinking about margin pressure, I, you need to jump to cash. If you’re having a concern about margin pressure, then your first question needs to be, okay, where am I on cash and do I actually have a cash problem? Because margin pressure, if you don’t have a cash problem, margin pressure, that’s something you can work through. Particularly if it’s transitory, your investors can make a call that they, they’re willing to accept some margin pressure in the short term to keep investing in something that they think is gonna have ROI in the longer term. They, you know, particularly in private equity, if there’s one bad year of margins, but you can explain that the reasons why and that you kept investing and that’s why costs stayed high, to have greater returns in later years, that’s okay. You can make that choice. Your investors need to make the choice.
Leadership needs to make the choice, but you, you can make that choice. But if it’s cash, there’s no choice to me be made if and if, uh, the margin pressures are have the same root costs that’s causing a potential cash issue, you need to immediately address that. And there are lots of different levers and, and it again, depends on business. One example I give in professional services, again, I lean towards that because I work in that space a lot, but the majority of cost of goods sold and all costs is people costs, but they’re much more variable than in other businesses because a large chunk of compensation in consulting firms is bonuses. So there are places where you can actually manage your margins. And that, that’s one example is managing your bonus pool to a margin. You’re not gonna be able to manage it to, to whatever margin you want, but there are different levers in different businesses where you can actually manage the margins that way.
Glenn Hopper:
Taking it back to cash, actually I was fighting the urge to turn around and try to find, uh, scaling up the <laugh> Vern Harnish book, you know, cash is king and all <laugh>, uh, behind me because that is, is such a key point. And it’s funny when in, especially in the SMB space, you deal a lot with C-suite folks who, uh, just look at the p and l as their cash and don’t even think about a ca you know, the cash flow forecast and all that 13 week cash flow forecast. What’s that? You know, <laugh>? Yeah,
Nate Saperia:
Actually I, I’m only continue taking off the rails here, if that’s okay. Uh, yeah,
Glenn Hopper:
Yeah.
Nate Saperia:
I do wanna talk cash for a minute. And you know, when in fp and a, we think about two different cash flow forecasts. One is the indirect, you go down to net income and then build out your operating cash flow, finance and cash flow and investing cash flow. And that’s really important. It’s important to do it for budgeting for all sorts of purposes, annual operating planning. But the direct cash flow, if you can do that, it, it is so granular going through every receipt and cash outflow, if you can do that one, you can see things coming that are gonna be massive issues in the business. You can see them before they, they happen. And then also the only way to do it is to know the business inside and out. So to one, it’s just really important for an FP 19 to be able to do a good 30 week cash flow direct forecast, but it also means, you know, the business inside out, which is going to make you a great business partner and hopefully a great storyteller if you know the business that well. So I took, took that off the rails a little bit there.
Glenn Hopper:
No, that’s great. Yeah, no pun intended with data rails, you took it off the data rails, <laugh> rails. Alright, so I wanna go to the audience questions in a minute, but in, actually, we’re gonna have a great transition here because one of the audience questions was related to this. So hearing you talk and knowing what my approach was, especially as a CFO where I could actually influence these kind of decisions. So first off, let me, uh, let me preface this with one of the first audience questions. Here it is from Damien. How can an FP and a function trust the financial data it’s using when it doesn’t control the data without spending all its time reconciling? So there’s that question. And then the question that I had, uh, as one of our original panel questions was, should fp and a or it own the data strategy? So taking those two sort of in context, tell me, tell me your thoughts there.
Nate Saperia:
Yeah, so I going on the first one first, and I’m specifically gonna talk about financial data here in fp and a, especially, you know, at head of fp and a level accounting and in particular the controller, basically be your best friend. You can be frenemies if you want, uh, which you probably are gonna be at times. But yeah, my, my last head of fp a role, the controller was literally my best friend. We shared an office, but we, we just had to spend so much time together and that was just the relationship between accounting and fp and a is so important and having the trust and be willing to say, Hey, this just like, it doesn’t look right to me. Can you take a look at that? And building that relationship is super important. The two are just so intertwined. So get to know your accounting department really well. Be nice to them, buy them gifts, but then, you know, push back when you need to and if something doesn’t look great, can you remind me the second question?
Glenn Hopper:
Yes. And I will, the funny thing, uh, and before we go go to that, if you talk to someone from like the baby boomer generation and you try to explain to them what fp NA is, they say, oh, a controller. So the interesting thing is, you know, we talked early on about how fp NA, you know, it’s, it’s a newish area if you think about sort of evolution of the CFO and that, um, and what, so controllers today are what CFOs were, you know, 25 years ago. Um, but, but fp and a, the initial stuff that was done was the controller would put out the monthly reports and then would get the follow up questions. And it’s funny because I, in my mind it’s really two different mindsets of, there’s the accountant mindset and then there’s the finance mindset where the accountant is everything balances.
Uh, there’s no ambiguity. It’s, and that’s how you want, obviously you <laugh> in in accounting, you want that, but in finance you can be more directionally right. You know, you’re, you’re living in, in forecast and all that. So it’s, it’s in, it’s, you can see how it’s split, but it’s, it’s funny that that started that they were asking the, the, the controllers to do that. And I think it probably, I, I would imagine it breaking a lot of controllers back in the day to say, I can’t make a a forecast that’s not accurate. And if I miss it by 2%, I’m, you know, it’s the end of the world.
Nate Saperia:
It’s a very different mindset. I do like to joke that I’m very proud that I am not licensed or certified to do anything. I guess literally not licensed certified to do. I guess it shouldn’t make me feel proud, but it kind of,
Glenn Hopper:
It’s that Groucho marks thing, right? Like I, I wouldn’t wanna join any club that would accept me as a member. Is that <laugh> is
Nate Saperia:
That being sent to the folks out there learning? There are a lot of credentials out there now that are fantastic and go get them. They’re fantastic. C
Glenn Hopper:
Yeah, and I would say multiple times in my career, looking back, I kind of wished I had that CPA and that fundamental accounting knowledge when I started because having to figure all this stuff out is on the fly was tough at times. Especially pe PE-backed companies and they’re wearing, you’re out and you, you’re like, I don’t know why we didn’t set that up as a prepaid <laugh>. Yeah, yeah, <laugh> very early in my career. Hopefully I’ve figured it out in 20 something years, but <laugh>,
Nate Saperia:
But we’re all still learning.
Glenn Hopper:
So the follow up question goes beyond gl and I don’t, maybe, maybe the, uh, not, not owning the data, maybe that was just limited to gl, but in, in my world, uh, I always trying to pull in other data, I would constantly go head to head, and this is after I hit the CFO role, I would go head to head with IT over who should own the data and the data strategy. And I’ve got my thoughts on it, but do you have a thought beyond GL of who, who yeah. Who should be the chief data officer, I guess.
Nate Saperia:
Absolutely. And yeah, I, I’m a hundred percent with you, like fp and a, it’s not just financial. I talked a lot about drivers and that’s coming from operating data. I do have a strong view on this. It’s not necessarily the, the approach that has been the case in my work history, but I, I think you need a CTO and I think it needs to, to own data. Everything has just gotten so complex and the, the risks are so high, like cybersecurity, risk data, governance risk, there’s the risks that are extremely high. And then there’s o the opportunities like 10 years ago, again, when we’re building data models in Power bi, you know, that that was cutting edge and it allowed us to do things that couldn’t be done before. But that was, that was out of necessity. We’re not in that period of necessity anymore. We’re in a period where data needs to be tightly managed and it, if Bill correctly, should just be in a much better position to do that. But I’m curious what your views are there.
Glenn Hopper:
So I think, you know, first off, I’d never wanna be a ciso. I don’t, that’s, you know, zero aren’t just to me. And also we have domain expertise in a certain area. So the expectation that we are also, you know, data scientists or, or understand, you know, machine learning pipelines and, and all the, all the stuff that goes into data. However, there’s an interesting, the, the reason I put that question on there is because at times I’ve seen the head of it, uh, CIO or CTO, you know, who whatever the, the role is because they own the data, they own the, the fuel. They think that they should be able to dictate how it’s used and the, and the data definitions around it. And to me, determining those KPIs and determining, you know, defining the source of truth sort of falls under the finance wheelhouse more than it does an IT person. So that’s where, and it was always, you know, I would always fight with sales and marketing because I never believed a, a single forecast that they gave me, and I would always fight with it because I didn’t want to accept, you know, what they, what data they thought I needed. I thought it should be just a, you know, full ownership, uh, of, of it and, and let finance define it.
Nate Saperia:
Yeah. And no, I, i, I agree with that and I know that’s a major pain point is, is sometimes if it owns it, sometimes you have a data request and it can take days, weeks to get the data back and that’s just not how fp and a operates. And I agree also, fp and a finance is responsible for strategic planning and financial outcomes. And given that, you know, we should have a really strong say in the data sources and the KPIs, the metrics and how they’re calculated, which I talked about earlier. So I guess what I’m saying is it’s not as simple as one versus the other. I still think at this point in time, the CTO it needs have a, a final say on data, but there should be a really, really close partnership there. And there needs to be a relationship where if finance is asking for something, they’re gonna get it and get it. Yeah.
Glenn Hopper:
Alright, let’s, let’s work our way through some of these other attendee questions. So Eric, he has a background doing m and a financial due diligence for quality of earnings and thoughts on leveraging the background to doing fp and a. And essentially they require almost the same skill sets. And I agree a hundred percent. And in my first fp and a role, I was primarily it was, I was in telecom and we were doing so many mergers that, um, I’ve really cut my teeth on m and a. And I think that, that that kind of work, and you probably have a similar experience working in with PE-backed companies. Uh, but what, what are your thoughts on that?
Nate Saperia:
I think it’s, it’s spot on. You know, I, I took my former firm through a sale process in 2022 and did four qvs over the course of that summer. Not that I let led them, I was responding to for requests for quality of earnings data and responses on questions. And you know, that background, it, it’s similar, you have to have deep knowledge of Excel. Like these are very complicated Excel workbooks and they understood how all the financial statements tie together and e even some QEs nowadays are not just financial, but getting into operational metrics. So I think it’s a really good background for fp and a. I’ve actually had on my most recent fp and a team people who came from, uh, Q of e uh, FDD background and you know, I think it’s great for it. Uh, I had to mess some bankers and I had FDD folks and I was the odd duck who had done either.
Glenn Hopper:
So Sumitra has a, a question about if the financial system you’re using doesn’t have AI capability and you wanna use it for board reports, are you concerned about confidential data? I actually <laugh> hosted a, a webinar, um, FPNA con yesterday. I kept everybody five minutes at later at the end so I could do my data ran. I’m not gonna do that again unless there’s people wanna stay five minutes later I’ll do it again, <laugh>, because I have a strong feeling about that. So I’m gonna skip that for now and say, please check out the fp a today podcast that is, is a recording of that. And, and we go into great detail. ’cause that was really AI focused. But in the interest of time of getting the other questions, um, Charles, uh, wants to know, what advice would you give to someone who just started his first fp and a internship? Nate, I bet you’ve got great insights here.
Nate Saperia:
Yeah, absolutely. So a company that has an fp a internship, they probably have a decently sized fp and a team that you can learn from. That’s actually, that’s the first time I’ve heard of an fp and a internship. So that, that’s really awesome. I’m a big advocate of getting fp and a more into the classroom at the undergraduate level. Learn as much as you can from that team because they’ve seen a lot for sure. And just learn as much as you can from them. Learning on the job is the best way to learn, raise your hand for the tough projects and be prepared to fail sometimes. And that that’s okay. ’cause if you’re not, if you’re not failing, you’re not making progress forward. So take the tough tasks, listen, learn, and you should have a really, really great experience.
Glenn Hopper:
And I will add to that, you’re so early in your career and, uh, I think that this is just, if, if I’m reading the tea leaves right now, lean heavily into data science. Now I know in big companies, certainly the size of a company that has an fp a intern, they probably also have a data science team. But rather than being downstream and a client of data science, if you learn the fundamentals of that, especially as automation and AI get more, uh, just widespread where it makes all this, uh, the barrier to entry of having to learn Python and R or and write SQL queries and all that as that barrier lowers, just like you have domain expertise in finance, that you know how to ask the right questions to tell the difference between net income and operating income and EBITDA and all that. If you know the fundamentals of data science, you know the right questions to ask about you come up with, you’d have better time series analysis forecast and all that. So I, I would add to that learn, uh, learn data science at this point. One pretty similar to this, similar, but it’s focused more on young fp NA managers. So Bonnie says, what are one or two things you wish you’d known or done differently early in your fp NA career and what would you pass on to young fp NA managers about that?
Nate Saperia:
Earlier in my fp a career when I was an early fp NA manager, I could not let go. I was too much of a control freak. And, and there’s no point of being a manager if you are not gonna let go and you’re still gonna do all the work yourself, trust your team, like I said before, they’re gonna make mistakes, but your job as a manager is to help them, one, to helps spot those mistakes before they get up to the CFO. And two is to help the team learn from their mistakes and grow as a team team. And it’s such a massive unlock. Once I started trusting my team more, the weight that was lifted off my shoulders, it, it was just immense. So, uh, trust your team. It’s tough, but there, there’s a reason they have that job and let them do it. Yeah,
Glenn Hopper:
That’s so true for any managers. Think about, you know, you wanna avoid the Peter principle and the idea it we’re, you know, as fp and a pros we’re like engineers, so yeah, maybe we got an MBA and we took some courses on, on soft skills and all that. But for, for me anyway, going back and I think it’s probably still the same, the draw to working in finance to working in fp and a is that a desire to build the models and to really dig in and do that sort of deep level of work. So when you’re, you have that first management role and you’re not the one who built the model, it’s hard to let go of, well let me go check formulas here and make sure that all the drivers carry through and that nothing’s broken and all that. And just to trust that team and sort of the same conversation with AI right now of, and, and rightfully so. I don’t, we’re not ready to trust AI in a whole lot of things, but getting to that trust with the team is, is very hard as that, you know, deep thinking model building finance person.
Nate Saperia:
Yeah. And, and one more thing on that is following through the formulas. That’s not where you’re gonna find, that’s not how you’re gonna find the big errors. The way you’re gonna find the big errors is being able to look down 30,000 feet and say this output, even if it’s printed on a sheet of paper, I know there’s something wrong with this output. It just doesn’t make sense. You know, pressing control and going through the formulas. It’s gonna be looking at a sheet of paper, looking at say your margin percent and just saying, Nope, no way I go back that model. Uh, so that’s another big one. Just try, do your best to, to just step back.
Glenn Hopper:
Alright, just a couple minutes left. I want to leave with kind of a forward thinking, like what something that people can walk away with. So everything we talked about where we see technology right now, ai, bi, all that, what emerging skills will define high performing fp and a analysts in the next say five years? And what should they be doing to develop those skills right now?
Nate Saperia:
So it, it’s of course gonna be, uh, one, I don’t think excel’s ever going away. You’re gonna have to pry it out in my cold dead end. So I didn’t <laugh> definitely learn excel, but to get up to speed, uh, on ai, get up to speed on automation, get up to speed on, on data science, but also I don’t think what fundamentally makes a great, uh, FPA person, like I said before of just, you know, that drive to get to the answer and that drive to learn. I don’t think that’s gonna change. And I think that’s what’s going to be the en enabler of staying on the cutting edge of the tools that you need to be on.
Glenn Hopper:
Well, we are right at time, so I think we’re gonna have to wrap up there. So that, that’ll conclude our, our session of the top 10 burning fp and a questions, um, I hope today gave everyone some fresh perspectives, practical ideas, and maybe even a few things to challenge your thinking. Big thank you to Nate for bringing the heat, sharing such thoughtful, experience driven insights. And of course, thank you to all of you in the audience for your questions, energy and engagement. This session was part of fp and a con presented by Data Rails. It will also be released a reminder as a special episode of the fp a Today podcast. So if you wanna revisit any part of it or share it with your team, keep an eye out for that episode drop in the coming weeks. And if you’d like to continue the conversation and connect, uh, disconnect with me or Nate on LinkedIn and we’d love to hear your thoughts, answer your follow-up questions or, or keep the discussion going. And again, our our condolences to swati and, um, thank the rest of you for, uh, for spending time with us and, and we’ll see you next time.
Nate Saperia:
Thanks, Glenn. Thanks to everyone.
